VIAF: Verification-based Integrity Assurance Framework for MapReduce

1. VIAF: Verification-based Integrity Assurance Framework for MapReduce Yongzhi Wang, Jinpeng Wei

2. MapReduce in Brief • Satisfying the demand for large scale data processing • It is a parallel programming model invented by Google in 2004 and become popular in recent years. • Data is stored in DFS(Distributed File System) • Computation entities consists of one Master and many Workers (Mapper and Reducer) • Computation can be divided into two phases: Map and Reduce

3. Word Count instance Hello, 4 Hello, 2

4. Integrity Vulnerability How to detect and eliminate malicious mappers to guarantee high computation integrity ? Straightforward approach: Duplication Non-collusive worker

5. Integrity vulnerability But how to deal with collusive malicious workers?

6. VIAF Solution • Duplication • Introduce trusted entity to do random verification

7. Outline • Motivation • System Design • Analysis and Evaluation • Related work • Conclusion

8. Architecture

9. Assumption • Trusted entities • Master • DFS • Reducers • Verifiers • Untrusted entities • Mappers • Information will not be tampered in network communication • Mappers’ result reported to the master should be consistent with its local storage (Commitment based protocol in SecureMR)

10. Solution • Idea: Duplication + Verification • Deterministically duplicate each task to TWO mappers to discern the non-collusive mappers • Non-deterministically verify the consistent result to discern the collusive mappers • The credit of each mapper is accumulated by passing verification • A Mapper become trustable only when its credit achieves Quiz Threshold • 100% accuracy in detecting non-collusive malicious mapper • The more verification applied on a mapper, the higher accuracy to determine whether it is collusive malicious.

11. Data flow control 2 3 5.a 1 4 6 4 3 1 5.b 2

12. Outline • Motivation • System Design • Analysis and Evaluation • Related work • Conclusion

13. Theoretical Analysis • Measurement metric (for each task) • Accuracy -- The probability the reducer receive a good result from mapper • Mapping Overhead – The average number of execution launched by the mapper • Verification Overhead – The average number of execution launched by the verifier

14. Accuracy vs Quiz Threshold Collusive mappers only, different p: • m – Malicious worker ratio • c – Collusive worker ratio • p – probability that two assigned collusive workers are in one collusion group and can commit a cheat • q – probability that two collusive workers commit a cheat • r – probability that a non collusive worker commit a cheat • v – Verification Probability • k – Quiz Threshold.

15. Overhead vs Quiz Threshold Collusive worker only: Overhead for each task stays between 2.0 to 2.4 Collusive worker only: Verification Overhead for each task stays between 0.2 to 0.207 when v is 0.2

16. Experimental Evaluation • Implementation based on Hadoop 0.21.0 • 11 virtual machines(512 MB of Ram, 40 GB disk each, Debian5.0.6 ) deployed on a 2.93 GHz, 8-core Intel Xeon CPU with 16 GB of RAM • word count application, 400 mapping tasks and 1 reduce task • Out of 11 virtual hosts • 1 is both the Master and the benign worker • 1 is the verifier • 4 are collusive workers (malicious ratio is 40%) • 5 are benign workers

17. Evaluation result Where c is 1.0, p is 1.0, q is 1.0, m is 0.40

18. Outline • Motivation • System Design • Analysis and Evaluation • Related work • Conclusion

19. Related work • Replication, sampling, checkpoint-based solutions were proposed in several distributed computing contexts • Duplicate based for Cloud: SecureMR(Wei et al, ACSAC ‘09), Fault Tolerance Middleware for Cloud Computing( Wenbinget al, IEEE CLOUD ‘10) • Quiz based for P2P: Result verification and trust based scheduling in peer-to-peer grids (S. Zhao et al, P2P ‘05) • Sampling based for Grid: Uncheatable Grid Computing (Wenliang et al, ICDCS’04) • Accountability in Cloud computing research • Hardware-based attestation: Seeding Clouds with Trust Anchors (Joshua et al, CCSW ‘10) • Logging and Auditing: Accountability as a Service for the Cloud(Jinhui, et al, ICSC ‘10)

20. Outline • Motivation • System Design • Analysis and Evaluation • Related work • Conclusion

21. Conclusion • Contributions • Proposed a Framework (VIAF) to defeat both collusive and non-collusive malicious mappers in MapReduce calculation. • Implemented the system and proved from theoretical analysis and experimental evaluation that VIAF can guarantee high accuracy while incurring acceptable overhead. • Future work • Further exploration without the assumption of trust of reducer. • Reuse the cached result to better utilize the verifier resource.

22. Thanks! Q&A